Generating testing instances for autonomous vehicles

ABSTRACT

Sensor data collected from an autonomous vehicle can be labeled using sensor data collected from an additional vehicle. Labeled sensor data can generate targeted testing instances for a trained machine learning model, where the trained machine learning model is used in generating control signals for an autonomous vehicle. In many implementations, targeted training instances can generate an accuracy value for the trained neural network model. Additionally or alternatively, the sensor suite on the additional vehicle can include a removable hardware pod which can be mounted on a variety of vehicles.

BACKGROUND

As computing and vehicular technologies continue to evolve,autonomy-related features have become more powerful and widelyavailable, and capable of controlling vehicles in a wider variety ofcircumstances. For automobiles, for example, the automotive industry hasgenerally adopted SAE International standard J3016, which designates 6levels of autonomy. A vehicle with no autonomy is designated as Level 0.With Level 1 autonomy, a vehicle controls steering or speed (but notboth), leaving the operator to perform most vehicle functions. WithLevel 2 autonomy, a vehicle is capable of controlling steering, speed,and braking in limited circumstances (e.g., while traveling along ahighway), but the operator is still required to remain alert and beready to take over operation at any instant, as well as to handle anymaneuvers such as changing lanes or turning. Starting with Level 3autonomy, a vehicle can manage most operating variables, includingmonitoring the surrounding environment, but an operator is stillrequired to remain alert and take over whenever a scenario the vehicleis unable to handle is encountered. Level 4 autonomy provides an abilityto operate without operator input, but only under certain conditionssuch as only in certain types of roads (e.g., highways) or only incertain geographic areas (e.g., a geofenced metropolitan area which hasbeen adequately mapped). Finally, Level 5 autonomy represents that avehicle is capable of operating without operator input under allconditions.

A fundamental challenge to any autonomy-related technology relates tocollecting and interpreting information about a vehicle's surroundingenvironment, along with planning and executing commands to appropriatelycontrol vehicle motion to safely navigate the vehicle through itscurrent environment. Therefore, continuing efforts are being made toimprove each of these aspects, and by doing so autonomous vehiclesincreasingly are able to safely and reliably operate in increasinglycomplex environments and accommodate both expected and unexpectedinteractions within an environment.

SUMMARY

The present disclosure is directed in part to the generation of testingdata for a machine learning model trained to generate control signals tocontrol an autonomous vehicle. In many implementations, information canbe collected from one or more additional vehicles and mapped toinformation collected from an autonomous vehicle. Autonomous vehicledata as used herein includes data streams captured from one or moresensors of an autonomous vehicle sensor suite as well as data capturedfrom the autonomous vehicle including data from a controller areanetwork (“CAN”) bus of an autonomous vehicle. Additional vehicle data asused herein includes data streams captured from a second vehicle (eitherautonomous or non-autonomous) temporally and/or physically located nearthe autonomous vehicle in addition to the autonomous vehicle. In manyimplementations, an additional vehicle can be a non-autonomous vehicleand additional vehicle data can include data streams collected from asensor suite of a removable hardware pod mounted on the non-autonomousvehicle as well as data streams collected by the removable hardware podfrom the non-autonomous vehicle itself, such as data collected from aCAN bus of the non-autonomous vehicle. In other implementations, anadditional vehicle can be an autonomous vehicle, and additional vehicledata can include data streams collected from a sensor suite of thecorresponding autonomous vehicle, as well as data streams collected fromthe autonomous vehicle such as data from a CAN bus of the autonomousvehicle. Additionally or alternatively, data streams can be collectedfrom an autonomous vehicle (e.g., autonomous vehicle data) and severaladditional vehicles (e.g., additional vehicle data) to quickly generatea large amount of collected data. For example, removable hardware podscan be mounted on several rental cars to quickly collect data in a newarea. The amount of data collected can be increased by mountingadditional removable hardware pods onto additional vehicles (i.e.,hardware pods mounted on ten vehicles can collect more data compared tohardware pod mounted on three vehicles).

In many implementations, an autonomous vehicle sensor suite can includea variety of sensors including: three dimensional (“3D”) positioningsensors (e.g., a Global Positioning System (“GPS”)), a radio detectionand ranging (“RADAR”) unit, a light detection and ranging (“LIDAR”)unit, an inertial measurement unit (“IMU”), one or more cameras, etc.LIDAR units in accordance with many implementations can include units tocollect a variety of specific types of LIDAR such as frequency-modulatedcontinuous-wave LIDAR and/or other types of specialized LIDAR. In avariety of implementations, a removable hardware pod is vehicle agnosticand therefore can be mounted on a variety of non-autonomous vehiclesincluding: a car, a bus, a van, a truck, a moped, a tractor trailer, asports utility vehicle, etc. While autonomous vehicles generally containa full sensor suite, in many implementations a removable hardware podcan contain a specialized sensor suite, often with fewer sensors than afull autonomous vehicle sensor suite, which can include: an IMU, 3Dpositioning sensors, one or more cameras, a LIDAR unit, etc.Additionally or alternatively, the hardware pod can collect data fromthe non-autonomous vehicle itself, for example, by integrating with thevehicle's CAN bus to collect a variety of vehicle data including:vehicle speed data, braking data, steering control data, etc. In someimplementations, removable hardware pods can include a computing devicewhich can aggregate data collected by the removable pod sensor suite aswell as vehicle data collected from the CAN bus, and upload thecollected data to a computing system for further processing (e.g.,uploading the data to the cloud). In many implementations, the computingdevice in the removable pod can apply a time stamp to each instance ofdata prior to uploading the data for further processing. Additionally oralternatively, one or more sensors within the removable hardware pod canapply a time stamp to data as it is collected (e.g., a LIDAR unit canprovide its own time stamp). Similarly, a computing device within anautonomous vehicle can apply a time stamp to data collected by theautonomous vehicle's sensor suite, and the time stamped autonomousvehicle data can be uploaded to the computer system for additionalprocessing.

Time stamped data from an autonomous vehicle and time stamped data froman additional vehicle (e.g., a second autonomous vehicle, anon-autonomous vehicle equipped with a removable hardware pod, etc.) canbe processed by a computing system. In some such implementations, thecomputing system can be remote to both the autonomous vehicle and theadditionally vehicle. Offloading processing of autonomous vehicle datato a remote computing device (i.e., offline processing) can leaveadditional computational power available for additional tasks to beperformed by a computing device in the autonomous vehicle in real time.

The computing system can determine overlapping data sets from theautonomous vehicle and the additional vehicle, i.e., data sets where theadditional vehicle is in the sensor perception area of the autonomousvehicle in an environment. In some such implementations, an additionalvehicle should be within two hundred fifty meters of the autonomousvehicle. An autonomous vehicle and an additional vehicle can move in andout of the vicinity of one another while collecting data. Portions ofdata sets can overlap where the additional vehicle is in the vicinity ofthe autonomous vehicle only at certain time stamps. In some suchimplementations, the entirety of the data sets can be processed. Inother implementations, only the portions of the data sets where thevehicles are within the sensor perception area of each other can beprocessed (i.e., only particular time stamps of the data sets areprocessed). Furthermore, the computing system can synchronize timestamps between the autonomous vehicle data and the additional vehicledata. Additionally or alternatively, the computing device can determinea location of the autonomous vehicle and the additional vehicle withinthe environment.

In many implementations, the computing system can generate labels (insome cases automatically) for the autonomous vehicle data utilizingadditional vehicle data. For example, the computing device can identifythe additional vehicle in data stream captured by the autonomous vehiclesensor suite (i.e., the autonomous vehicle data). Once located, thecomputing device can map data gathered at a particular time stamp by theadditional vehicle (such as data collected from a non-autonomous vehiclemounted with a removable hardware pod) as well as other knowninformation about the additional vehicle (e.g., vehicle type, vehiclemake, vehicle model, vehicle year, vehicle dimensions, removable podposition on the vehicle, etc.) to the location of additional vehicleidentified in the autonomous vehicle data. As another example, anadditional vehicle can record vehicle velocity from data gathered via aCAN bus. This velocity measurement can be mapped to the location of theadditional vehicle in the autonomous vehicle data stream, thusautomatically generating a velocity label for the additional vehicle inthe autonomous vehicle data stream. Additionally or alternatively, atight bounding box can be generated around the additional vehicle in theautonomous vehicle data stream by utilizing, for example, one or morevehicle measurements provided in the additional vehicle data.

In many implementations, a computing system can train a machine learningmodel for use in generating one or more control signals for theautonomous vehicle with training instances which include an instance ofautonomous vehicle data where the additional vehicle can be captured byone or more sensors of the autonomous vehicle sensor suite and acorresponding label for the additional vehicle (e.g., known informationabout the additional vehicle, data captured using a removable hardwarepod mounted on the additional vehicle, etc.). In a variety ofimplementations, the one or more labels can be compared with a predictedoutput generated by the machine learning model, such as a neural networkmodel, to update one or more weights (i.e., through backpropagation) ofthe machine learning model. In addition thereto, targeted labeledtraining data can be generated for specific autonomous vehicle tasks.Autonomous vehicle tasks can include control signals indicating a routechange action, a planning action, and/or other autonomous vehicleactions which are generated in response to data collected from one ormore autonomous vehicle sensors. Waiting for training data to begathered for some autonomous vehicle tasks, such as correctly respondingto maneuver around a car parallel parking on the side of the road, cantake extended periods of time (e.g., weeks, months, etc.) as the tasksoccurs at random times. In contrast, in various implementations, anadditional vehicle can be driven near the autonomous vehicle to generatethe training data in a few days and/or hours. For example, theadditional vehicle can repeatedly parallel park on the side of the roadahead of the autonomous vehicle, and data gathered by both theadditional vehicle and the autonomous vehicle can generate training datato train a neural network model for use in controlling an autonomousvehicle when a nearby car is parallel parking. Additionally oralternatively, a seemingly endless diversity of data to generatetargeted training instances for any of a variety of autonomous vehicletasks can be generated by driving an additional vehicle around anautonomous vehicle in accordance with many implementations. In someinstances, targeted labeled training data can include vehicle behaviorsat intersections, on-coming lane intrusion, increased braking and otherunusual behavior wherein data may be collected for enhanced and robustdivers training data.

Similarly, for example, a computing device can generate testing data fora trained machine learning model, where testing data includes aninstance of an autonomous vehicle data where the additional vehicle iscaptured by one or more sensors of the autonomous vehicle (i.e.,training input) and a corresponding label for the additional vehicle(i.e., known output). For example, a machine learning model can betrained to predict one or more control signals to perform a specificautonomous vehicle task, such as operating the autonomous vehicle at afour way stop. Testing data can be generated by repeatedly driving oneor more additional vehicles through a four way stop in an environmentwith an autonomous vehicle. The accuracy of the machine learning modelto predict control signal(s) for the autonomous vehicle can be evaluatedfor a variety of tasks using testing data generated in accordance withmany implementations.

In many implementations, training and/or testing machine learning modelsfor use in autonomous vehicles requires a large amount of data. Testinginstances and/or training instances have traditionally been generated byusers annotating snapshots of sensor data (i.e., a human generates alabel). Labeling data by hand one instance of sensor data at a time istime consuming as well as computationally expensive to visualize thesensor data in a way that is comprehensible to a human. Additionally oralternatively, a user labeling sensor data can be error prone. Forexample, the repetitive nature of the labeling task can cause a userlabeling the sensor data to miss labels and/or generate less accuratelabels over time. In contrast, automatically generated labeled datastreams occurs much quicker than user labeling, does not use additionalcomputational resources to display sensor data to a user, is not proneto errors, etc.

Additionally or alternatively, some implementations disclosed herein canlabel data that a user is unable to label. The difficulty to accuratelylabel an object increases as distance from the object increases. Knowndimensions of an additional vehicle (e.g., known dimensions for the makeand model of the vehicle a removable hardware pod is mounted onto) canbe utilized in generating a bounding box around the vehicle in theautonomous vehicle data stream. Automatic labeling systems can generatea more precise bounding box around an additional vehicle than a user,especially at a great distance. Furthermore, partial object occlusionsfrom other elements in the environment can prevent a user for accuratelylabeling an object. Automatic labeling systems, using known dimensionsof an additional vehicle, can generate an accurate label despite apartial occlusion of the additional vehicle. Additionally oralternatively, relative speed of an object can induce motion blur todata collected by the sensor suite of the autonomous vehicle which canmake an object more difficult for a user to label. In contract,automatic labeling systems by having knowledge about objectmeasurements, can accurately label rapidly moving objects.

In some such implementations, automatically generated labels can be usedin training instances and/or testing instances. Additionally oralternatively, highly accurate automatically generated labels canimprove the accuracy of testing instances and/or training instances formachine learning models. More accurate testing and/or training instanceswill lead to a more accurate machine learning model. In some suchimplementations, a more accurate machine learning model can increase thesafety of users riding in an autonomous vehicle, riding in othervehicles in the vicinity of the autonomous vehicle, walking in thevicinity of the autonomous vehicle, and/or additional user interactionswith the autonomous vehicle. Additionally or alternatively, testinginstances in accordance with implementations disclosed herein can savecomputational resources (e.g., processor cycles, battery power, etc.) byending the training of a machine learning model once an accuracy of themachine learning model is determined. Furthermore, the likelihood ofoverfitting a machine learning model to training instances is reduced byending training of the model once the accuracy of the model has beenverified.

The above description is provided as an overview of variousimplementations disclosed herein. Those various implementations, as wellas additional implementations are described in more detail herein.

In some implementations, a method of generating testing instances for aneural network model trained for an autonomous vehicle includesreceiving autonomous vehicle data using an autonomous vehicle sensorsuite on an autonomous vehicle. The method further includes receivingadditional vehicle data using an additional vehicle sensor suite on anadditional vehicle, where the additional vehicle is in an environmentwith the autonomous vehicle. The method further includes determining aninstance of autonomous vehicle data wherein at least one of the sensorsof the autonomous vehicle sensor suite detect the additional vehicle inthe environment. The method further includes determining an instance ofadditional vehicle data which temporally corresponds with the instanceof autonomous vehicle data where the autonomous vehicle detects theadditional vehicle. The method further includes generating a pluralityof testing instances for a trained machine learning model, where eachtesting instance includes the instance of autonomous vehicle where theautonomous vehicle detects the additional vehicle and a label indicatinga current state of at least one attribute of the additional vehicleusing the determined corresponding instance of additional vehicle data.

These and other implementations of the technology disclosed herein caninclude one or more of the following features.

In some implementations, the method further includes testing the trainedmachine learning model, by applying the autonomous vehicle data portionof the testing instance as input to the trained machine learning modelto generate output of the machine learning model. In some versions ofthose implementations, the method further includes determining anaccuracy value of the machine learning model by determining a differencebetween the output of the machine learning model and the label portionof the testing instance.

In some implementations, the method further includes receivingadditional vehicle data collected from a plurality of additionalvehicles in the environment with the autonomous vehicle. In someversions of those implementations, the method further includesdetermining an instance of the autonomous vehicle data where at leastone of the sensors in the autonomous vehicle sensor suite detects eachadditional vehicle in the plurality of additional vehicles. In someversions of those implementations, the method further includesdetermining an instance in the additional vehicle data corresponding toeach additional vehicle which temporally correlates with the instance ofautonomous vehicle data where each of the additional vehicles aredetected by the autonomous vehicle. In some versions of thoseimplementations, the method further includes generating a plurality oftesting instances for the trained machine learning model, where eachtesting instance includes the instance in the autonomous vehicle datawhere the autonomous vehicle detects each of the additional vehicles anda plurality of labels, each label indicating a current state of at leastone attribute of the additional vehicle using the determinedcorresponding instance of additional vehicle data.

In some implementations, the method further includes determining alocation of the autonomous vehicle in the environment in each instanceof the autonomous vehicle data and a location of the additional vehiclein the environment in each instance of the additional vehicle data byutilizing GPS data from each instance in the autonomous vehicle data andGPS data from each instance in the additional vehicle data.

In some implementations, the method further includes determining alocation of the autonomous vehicle in the environment in each instancein the autonomous vehicle data and a location of the additional vehiclein the environment by localizing the autonomous vehicle and theadditional vehicle using one or more common landmarks in the environmentidentified in the instance of the autonomous vehicle data and instanceof the additional vehicle data.

In some implementations, the additional vehicle is a second autonomousvehicle.

In some implementations, the sensor suite on the autonomous vehiclecomprises at least a Global Positioning System (GPS) unit, a radiodirection and ranging (RADAR) unit, a light detection and ranging(LIDAR) unit, one or more cameras, and an inertial measurement (IMU)unit. In some versions of those implementations, autonomous vehicle datacomprises at least GPS data, RADAR data, LIDAR data, one or more imagesfrom the one or more cameras, and IMU data.

In some implementations, the additional vehicle is a non-autonomousvehicle and the sensor suite on the additional vehicle is a removablehardware pod, wherein the removable hardware pod is mounted onto theadditional vehicle. In some versions of those implementations, theadditional vehicle is selected from a group consisting of a car, a van,a truck, a bus, a motorcycle, and a tractor trailer. In some versions ofthose implementations, the removable hardware pod comprises at least aGlobal Positioning System (GPS) unit, a light detection and ranging(LIDAR) unit, and one or more cameras. In some versions of thoseimplementations, additional vehicle data comprises at least GPS data,LIDAR data, one or more images from one or more cameras, IMU data, CANbus data, and known additional vehicle data. In some versions of thoseimplementations, known additional vehicle data is selected from a groupconsisting of a vehicle make, a vehicle model, a vehicle color, avehicle year, one or more vehicle dimension measurements, and a positionof where the vehicle agnostic hardware pod is mounted on the additionalvehicle, and combinations thereof.

In some implementations, a plurality of removable hardware pods aremounted onto the additional vehicle. In some versions of thoseimplementations, the method further includes receiving additionalvehicle data from each of the plurality of removable hardware podsmounted onto the additional vehicle.

In addition, some implementations include one or more processors (e.g.,central processing unit(s) (“CPU”(s)), graphics processing unit(s)(“GPU”(s)), and/or tensor processing unit(s) (“TPU”(s)) of one or morecomputing devices, where the one or more processors are operable toexecute instructions stored in associated memory, and where theinstructions are configured to cause performance of any of the methodsdescribed herein. Some implementations also include one or morenon-transitory computer readable storage media storing computerinstructions executable by one or more processors to perform any of themethods described herein.

It should be appreciated that all combinations of the foregoing conceptsand additional concepts described in greater detail herein arecontemplated as being part of the subject matter disclosed herein. Forexample, all combinations of claimed subject matter appearing at the endof this disclosure are contemplated as being part of the subject matterdisclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example environment in which implementationsdisclosed herein may be implemented.

FIG. 2A illustrates a block diagram of a removable hardware pod in whichimplementations disclosed herein may be implemented.

FIG. 2B illustrates an example removable hardware pod in whichimplementations disclosed herein may be implemented.

FIG. 2C illustrates an example removable hardware pod mounted onto avehicle in which implementations herein may be implemented.

FIG. 3 illustrates an example computing system in which implementationsdisclosed herein may be implemented.

FIG. 4 illustrates another example of a computing system in whichimplementations disclosed herein may be implemented.

FIG. 5 illustrates an example image of an autonomous vehicle in anenvironment with a non-autonomous vehicle in which implementationsdisclosed herein may be implemented.

FIG. 6 illustrates another example image of an autonomous vehicle in anenvironment with a non-autonomous vehicle in which implementationsdisclosed herein may be implemented.

FIG. 7 illustrates another example image of an autonomous vehicle in anenvironment with a non-autonomous vehicle in which implementationsdisclosed herein may be implemented.

FIG. 8 illustrates an example image of an autonomous vehicle in anenvironment with multiple non-autonomous vehicles in whichimplementations disclosed herein may be implemented.

FIG. 9 is a flowchart illustrating an example process of performingselected aspects of the present disclosure, in accordance with variousimplementations.

FIG. 10 is a flowchart illustrating another example process ofperforming selected aspects of the present disclosure, in accordancewith various implementations.

DETAILED DESCRIPTION

Referring to FIG. 1 , an example autonomous vehicle 100 within which thevarious techniques disclosed herein may be implemented. Vehicle 100, forexample, may include a powertrain 102 including a prime mover 104powered by an energy source 106 and capable of providing power to adrivetrain 108, as well as control system 110 including a directioncontrol 112, a powertrain control 114, and brake control 116. Vehicle100 may be implemented as any number of different types of vehicles,including vehicles capable of transporting people and/or cargo, andcapable of traveling by land, by sea, by air, underground, undersea,and/or in space, and it will be appreciated that the aforementionedcomponents 102-116 can vary widely based upon the type of vehicle withinwhich these components are utilized.

The implementations discussed hereinafter, for example, will focus on awheeled land vehicle such as a car, van, truck, bus, etc. In suchimplementations, the prime mover 104 may include one or more electricmotors and/or an internal combustion engine (among others). The energysource may include, for example, a fuel system (e.g., providinggasoline, diesel, hydrogen, etc.), a battery system, solar panels orother renewable energy source, and/or a fuel cell system. Drivetrain 108include wheels and/or tires along with a transmission and/or any othermechanical drive components suitable for converting the output of primemover 104 into vehicular motion, as well as one or more brakesconfigured to controllably stop or slow the vehicle 100 and direction orsteering components suitable for controlling the trajectory of thevehicle 100 (e.g., a rack and pinion steering linkage enabling one ormore wheels of vehicle 100 to pivot about a generally vertical axis tovary an angle of the rotational planes of the wheels relative to thelongitudinal axis of the vehicle). In some implementations, combinationsof powertrains and energy sources may be used (e.g., in the case ofelectric/gas hybrid vehicles), and in some instances multiple electricmotors (e.g., dedicated to individual wheels or axles) may be used as aprime mover.

Direction control 112 may include one or more actuators and/or sensorsfor controlling and receiving feedback from the direction or steeringcomponents to enable the vehicle 100 to follow a desired trajectory.Powertrain control 114 may be configured to control the output ofpowertrain 102, e.g., to control the output power of prime mover 104, tocontrol a gear of a transmission in drivetrain 108, etc., therebycontrolling a speed and/or direction of the vehicle 100. Brake control116 may be configured to control one or more brakes that slow or stopvehicle 100, e.g., disk or drum brakes coupled to the wheels of thevehicle.

Other vehicle types, including but not limited to airplanes, spacevehicles, helicopters, drones, military vehicles, all-terrain or trackedvehicles, ships, submarines, construction equipment etc., willnecessarily utilize different powertrains, drivetrains, energy sources,direction controls, powertrain controls and brake controls, as will beappreciated by those of ordinary skill having the benefit if the instantdisclosure. Moreover, in some implementations some of the components canbe combined, e.g., where directional control of a vehicle is primarilyhandled by varying an output of one or more prime movers. Therefore,implementations disclosed herein are not limited to the particularapplication of the herein-described techniques in an autonomous wheeledland vehicle.

In the illustrated implementation, full or semi-autonomous control overvehicle 100 is implemented in a vehicle control system 120, which mayinclude one or more processors 122 and one or more memories 124, witheach processor 122 configured to execute program code instructions 126stored in a memory 124. The processors(s) can include, for example,graphics processing unit(s) (“GPU(s)”)) and/or central processingunit(s) (“CPU(s)”).

Sensors 130 may include various sensors suitable for collectinginformation from a vehicle's surrounding environment for use incontrolling the operation of the vehicle. For example, sensors 130 caninclude RADAR unit 134, LIDAR unit 136, a 3D positioning sensors 138,e.g., a satellite navigation system such as GPS, GLONASS, BeiDou,Galileo, Compass, etc.

The 3D positioning sensors 138 can be used to determine the location ofthe vehicle on the Earth using satellite signals. Sensors 130 canoptionally include a camera 140 and/or an IMU 142. The camera 140 can bea monographic or stereographic camera and can record still and/or videoimages. The IMU 142 can include multiple gyroscopes and accelerometerscapable of detecting linear and rotational motion of the vehicle inthree directions. One or more encoders (not illustrated), such as wheelencoders may be used to monitor the rotation of one or more wheels ofvehicle 100.

The outputs of sensors 120 may be provided to a set of controlsubsystems 150, including, a localization subsystem 152, a planningsubsystem 156, a perception subsystem 154, and a control subsystem 158.Localization subsystem 152 is principally responsible for preciselydetermining the location and orientation (also sometimes referred to as“pose”) of vehicle 100 within its surrounding environment, and generallywithin some frame of reference. The location of an autonomous vehiclecan be compared with the location of an additional vehicle in the sameenvironment as part of generating labeled autonomous vehicle data.Perception subsystem 154 is principally responsible for detecting,tracking, and/or identifying elements within the environment surroundingvehicle 100. Planning subsystem 156 is principally responsible forplanning a trajectory for vehicle 100 over some timeframe given adesired destination as well as the static and moving objects within theenvironment. A machine learning model in accordance with severalimplementations can be utilized in planning a vehicle trajectory.Control subsystem 158 is principally responsible for generating suitablecontrol signals for controlling the various controls in control system100 in order to implement the planned trajectory of the vehicle 100.Similarly, a machine learning model can be utilized to generate one ormore signals to control an autonomous vehicle to implement the plannedtrajectory.

It will be appreciated that the collection of components illustrated inFIG. 1 for vehicle control system 120 is merely exemplary in nature.Individual sensors may be omitted in some implementations. Additionallyor alternatively, in some implementations multiple sensors of typesillustrated in FIG. 1 may be used for redundancy and/or to coverdifferent regions around a vehicle, and other types of sensors may beused. Likewise, different types and/or combinations of controlsubsystems may be used in other implementations. Further, whilesubsystems 152-158 are illustrated as being separate from processor 122and memory 124, it will be appreciated that in some implementations,some or all of the functionality of a subsystem 152-158 may beimplemented with program code instructions 126 resident in one or morememories 124 and executed by one or more processors 122, and that thesesubsystems 152-158 may in some instances be implemented using the sameprocessor(s) and/or memory. Subsystems in some implementations may beimplemented at least in part using various dedicated circuit logic,various processors, various field programmable gate arrays (“FPGA”),various application-specific integrated circuits (“ASIC”), various realtime controllers, and the like, as noted above, multiple subsystems mayutilize circuitry, processors, sensors, and/or other components.Further, the various components in vehicle control system 120 may benetworked in various manners.

In some implementations, vehicle 100 may also include a secondaryvehicle control system (not illustrated), which may be used as aredundant or backup control system for vehicle 100. In someimplementations, the secondary vehicle control system may be capable offully operating autonomous vehicle 100 in the event of an adverse eventin vehicle control system 120, whine in other implementations, thesecondary vehicle control system may only have limited functionality,e.g., to perform a controlled stop of vehicle 100 in response to anadverse event detected in primary vehicle control system 120. In stillother implementations, the secondary vehicle control system may beomitted.

In general, an innumerable number of different architectures, includingvarious combinations of software, hardware, circuit logic, sensors,networks, etc. may be used to implement the various componentsillustrated in FIG. 1 . Each processor may be implemented, for example,as a microprocessor and each memory may represent the random accessmemory (“RAM”) devices comprising a main storage, as well as anysupplemental levels of memory, e.g., cache memories, non-volatile orbackup memories (e.g., programmable or flash memories), read-onlymemories, etc. In addition, each memory may be considered to includememory storage physically located elsewhere in vehicle 100, e.g., anycache memory in a processor, as well as any storage capacity used as avirtual memory, e.g., as stored on a mass storage device or anothercomputer controller. One or more processors illustrated in FIG. 1 , orentirely separate processors, may be used to implement additionalfunctionality in vehicle 100 outside of the purposes of autonomouscontrol, e.g., to control entertainment systems, to operate doors,lights, convenience features, etc.

In addition, for additional storage, vehicle 100 may also include one ormore mass storage devices, e.g., a removable disk drive, a hard diskdrive, a direct access storage device (“DASD”), an optical drive (e.g.,a CD drive, a DVD drive, etc.), a solid state storage drive (“SSD”),network attached storage, a storage area network, and/or a tape drive,among others. Furthermore, vehicle 100 may include a user interface 164to enable vehicle 100 to receive a number of inputs from and generateoutputs for a user or operator, e.g., one or more displays,touchscreens, voice and/or gesture interfaces, buttons and other tactilecontrols, etc. Otherwise, user input may be received via anothercomputer or electronic device, e.g., via an app on a mobile device orvia a web interface.

Moreover, vehicle 100 may include one or more network interfaces, e.g.,network interface 162, suitable for communicating with one or morenetworks 170 (e.g., a Local Area Network (“LAN”), a wide area network(“WAN”), a wireless network, and/or the Internet, among others) topermit the communication of information with other computers andelectronic device, including, for example, a central service, such as acloud service, from which vehicle 100 receives environmental and otherdata for use in autonomous control thereof. In many implementations,data collected by one or more sensors 130 can be uploaded to computingsystem 172 via network 170 for additional processing. In some suchimplementations, a time stamp can be added to each instance of vehicledata prior to uploading. Additional processing of autonomous vehicledata by computing system 172 in accordance with many implementations isdescribed with respect to FIG. 3 and FIG. 4 .

Each processor illustrated in FIG. 1 , as well as various additionalcontrollers and subsystems disclosed herein, generally operates underthe control of an operating system and executes or otherwise relies uponvarious computer software applications, components, programs, objects,modules, data structures, etc., as will be described in greater detailbelow. Moreover, various applications, components, programs, objects,modules, etc. may also execute on one or more processors in anothercomputer coupled to vehicle 100 via network 170, e.g., in a distributed,cloud-based, or client-server computing environment, whereby theprocessing required to implement the functions of a computer program maybe allocated to multiple computers and/or services over a network.

In general, the routines executed to implement the variousimplementations described herein, whether implemented as part of anoperating system or a specific application, component, program, object,module or sequence of instructions, or even a subset thereof, will bereferred to herein as “program code”. Program code typically comprisesone or more instructions that are resident at various times in variousmemory and storage devices, and that, when read and executed by one ormore processors, perform the steps necessary to execute steps orelements embodying the various aspects of the invention. Moreover, whileimplementations have and hereinafter will be described in the context offully functioning computers and systems, it will be appreciated that thevarious implementations described herein are capable of beingdistributed as a program product in a variety of forms, and thatimplementations can be implemented regardless of the particular type ofcomputer readable media used to actually carry out the distribution.Examples of computer readable media include tangible, non-transitorymedia such as volatile and non-volatile memory devices, floppy and otherremovable disks, solid state drives, hard disk drives, magnetic tape,and optical disks (e.g., CD-ROMs, DVDs, etc.) among others.

In addition, various program code described hereinafter may beidentified based upon the application within which it is implemented ina specific implementation. However, it should be appreciated that anyparticular program nomenclature that follows is used merely forconvenience, and thus the invention should not be limited to use solelyin any specific application identified and/or implied by suchnomenclature. Furthermore, given the typically endless number of mannersin which computer programs may be organized into routines, procedures,methods, modules, objects, and the like, as well as the various mannersin which program functionality may be allocated among various softwarelayers that are resident within a typical computer (e.g., operatingsystems, libraries, API's, applications, applets, etc.), it should beappreciated that the invention is not limited to the specificorganization and allocation of program functionality described herein.

Those skilled in the art, having the benefit of the present disclosure,will recognize that the exemplary environment illustrated in FIG. 1 isnot intended to limit implementations disclosed herein. Indeed, thoseskilled in the art will recognize that other alternative hardware and/orsoftware environments may be used without departing from the scope ofimplementations disclosed herein.

Referring to FIG. 2A, an additional example environment is illustratedin which implements disclosed herein may be implemented. Removablehardware pod 200 in FIG. 2A includes external removable pod hardware 202and vehicle cabin removable pod hardware 216. External removable podhardware 202 can include a variety of sensors to collect environmentaldata. In a variety of implementations, a pod sensor suite can includeLIDAR unit 204, wide field of view camera 206, narrow field of viewcamera 208, 3D positioning sensors 212 such one or more GlobalNavigation Satellite Systems including: GPS, GLONASS, BeiDou, Galileo,Compass, etc., IMU 214, etc. In a variety of implementations, a printedcircuit board (“PCB”) 210 can be utilized to provide connections betweenvarious sensors within the sensor suite of external removable podhardware 202. In a variety of implementations, one or more components ofexternal removable pod hardware 202 can be contained in hardware housing256 as illustrated in FIG. 2B.

Vehicle cabin removable pod hardware 216 can be separate from themountable pod hardware. In some implementations, vehicle cabin removablepod hardware 216 can include USB adapters 218, 220, computing device222, AC adapter 224, 12V vehicle receptacle 226, OBDII port 228, etc.Computing device 222 can include a variety of devices including a laptopcomputing device, a tablet computing device, a cellular telephonecomputing device, etc.

The vehicle itself can power external removable pod hardware 202 and/orcomputing device 222. In some such implementations, 12V vehiclereceptacle 226 can connect to PCB 210 to provide power to externalremovable pod hardware 202. Similarly, 12V vehicle receptacle 226 canprovide power to AC adapter 224. In some such implementations, ACadapter 224 can provide power to computing device 222. In otherimplementations, the hardware pod can receive power from one or more USBports in the vehicle, or various other electrical connections with inthe vehicle, such as a vehicle taillight, etc. (not illustrated).Furthermore, computing device 222 can include one or more USB ports,each of which can connect to one or more USB adapters such as USBadapter 218, 220.

Environmental data collected by one or more cameras (e.g., cameras206/208) can be transmitted via a USB adapter (e.g., USB adapters218/220) to computing device 222. Additionally or alternatively, one ormore cameras can receive power using a USB adapter. Furthermore,computing device 222 can transmit one or more control signals to one ormore cameras utilizing connections made by USB adapters. For example,wide field of view camera 206 can transmit environmental data tocomputing device 222, receive command signals from computing device 222,and/or be powered utilizing USB adapter 218. Similarly, narrow field ofview camera 208 can transmit environmental data to computing device 222,receive command signals from computing device 222, and/or be poweredutilizing USB adapter 220. In some implementations, multiple cameras canconnect to computing device 212 via a single USB adapter. Additionallyor alternatively, in many implementations any of a variety of camerascan be used including the aforementioned wide field of view camera 206and narrow field of view camera 208 as well as a light field camera, athermal imaging camera, etc. In many implementations, one or morecameras can connect directly to PCB 210 (not illustrated).

Computing device 222 can connect to PCB 210 in external removable podhardware 202 via an Ethernet connection. In many implementations, anEthernet connection can be a wired connection. Additionally oralternatively, an Ethernet connection can be a wireless connection.Computing device 222 can transmit information to PCB 210 as well asreceive environmental data collected by a variety of sensors in theexternal removable hardware pod 202 via the Ethernet connection. In someimplementations, 3D positioning sensors 212 can be integrated directlyinto PCB 210. In other implementations, 3D positioning sensors 212 canbe separate and connect with PCB 210 (not illustrated).

PCB 210 can provide power (for example 12V) to a variety of sensorsincluding 3D positioning sensors 212, LIDAR unit 204, IMU 214, widefield of view camera 206 (not illustrated), narrow field of view camera208 (not illustrated), etc. In many implementations, PCB 210 cansynchronize timing with LIDAR unit 204 via a serial pulse per second(“PPS”) connection. Additionally or alternatively, PCB 210 and LIDARunit 204 can transmit and receive data via an Ethernet connection. Forexample, environmental data collected by LIDAR unit 204 can betransmitted to PCB 210 via an Ethernet connection. In variousimplementations, PCB 210 can transmit and receive information with IMU214. In many implementations, PCB 210 can interface with vehicle dataports, such as OBDII port 228, to additionally collect a variety ofcorresponding vehicle data. Additionally or alternatively, computingdevice 222 can interface with a vehicle data port including OBDII port228 to collect a variety of additional vehicle data (not pictured) suchas information collected from the additional vehicle's CAN bus.Additional vehicle data in accordance with many implementations caninclude: vehicle speed data, braking data, steering control data, etc.

In a variety of implementations, environmental data and/or vehicle datacan be appended with a time stamp by PCB 210, computing device 222,and/or individual sensors (e.g., LIDAR units can generate their own timestamps). Additionally or alternatively, time stamped environmentaland/or time stamped vehicle data can be combined and uploaded to thecloud using computing device 222 in conjunction with known communicationpathways and hardware. In some such implementations, the removable podcan combine time stamped environmental data and time stamped vehicledata by creating an instance of data at each time step (i.e., eachinstance of data can include environmental data at a specific time stamp(if any) as well as vehicle data at the same specific time stamp (ifany)).

FIG. 2B illustrates an image 250 of a removable pod in accordance withimplementations disclosed herein. Removable pod 252 can include avariety of elements such as one or more sensors 254, hardware housing256, removable pod mount(s) 258, etc. One or more sensors 254 which canbe utilized in removable pod 252 are illustrated in FIG. 2A. In avariety of implementations, a LIDAR unit can be the highest point on aremovable pod 252. Hardware housing 256 can protect various portions ofremovable pod 252 from weather elements such as rain, snow, wind, hottemperatures, cold temperatures, etc. A variety of shapes of hardwarehousing can be utilized in removable pod 252 in addition oralternatively to hardware housing 256 as illustrated in FIG. 2B. Forexample, hardware housing 256 can include two or more housing units tohouse separate portions of the removable pod 252 as well as a differentshaped hardware housing 256.

In a variety of implementations, removable pod mount(s) 258 can attachremovable pod 252 to a vehicle. Removable pod mount(s) 258 can beadjusted such that sensors within a removable pod 252 can collect datafrom a preferred defined position and/or location. For example,removable pod mount(s) 258 can be adjusted to compensate for differentcurvatures between different vehicles. Removable pod mount(s) 258 canattach removable pod 252 to a vehicle in a variety of ways including:suction cups, magnets, adhesives (e.g., glue, tape), etc. In someimplementations, removable pod mount(s) 258 can be a single mount.Varying number of mounts can be included in removable pod 252 inaccordance with various implementations. For example, removable pod 252can include two removable pod mounts 258, three removable pod mounts258, four removable pod mounts 258, etc. In many implementations, threeremovable pod mounts 258 can be arranged in a tripod styleconfiguration.

FIG. 2C illustrates an image of a removable pod mounted on a vehicle inaccordance with implementations disclosed herein. Combined vehicle andpod 280 can include removable pod 252 mounted atop a vehicle 282 in apredefined preferred position. In a variety of implementations,removable pod 252 is vehicle agnostic and can be mounted on a variety ofvehicles 282 including: cars, trucks, motorcycles, sports utilityvehicles, vans, mopeds, tractor trailers, busses, motorized scooters,etc. In some implementations, components linked with removable pod 252can be inside vehicle 282. For example, vehicle cabin removable podhardware 216 (as illustrated in FIG. 2A) can be inside the vehicle. As afurther example, vehicle cabin removable pod hardware 216 can be insidea car, sports utility vehicle, truck, bus, etc. In some such examples, alaptop computer can be utilized as computing device 222 in removable podhardware 216, and the laptop computer can be stored inside the vehicle.In other implementations, a vehicle can lack an enclosed interior andvehicle cabin removable pod hardware can be stored “in” the vehicle inadditional ways. For example, a motorcycle can utilize a cellulartelephone computing device as computing device 222 in removable podhardware 116. In some such examples, the motorcycle rider can carry thecellular telephone in a pocket. In implementations, the vehicle cabinremovable pod hardware 216 can be combined with the external removablepod hardware. Alternatively, various portions of the external removablepod hardware 202 can be combine with selected portions of the vehiclecabin removable pod hardware 216.

In many implementations, several removable pods 252 can be mounted tovehicle 282 to increase the density of data collection. For example, acar can have a removable pod 252 mounted to its front right side and anadditional removable pod 252 can be mounted to its front left side. Asanother example, a vehicle pulling a trailer can have a removable pod252 mounted to the vehicle and a second removable pod 252 mounted to thetrailer to capture, for example, additional data when the trailer movesin a different direction from the vehicle. As a further example, a buscan have three removable pods 252 mounted along the top to captureadditional environmental data a single removable pod 252 couldpotentially miss because of the larger size of a bus when compared to asmaller vehicle such as a car. For example, line of sight perspectivesof a pod on a larger vehicle could be restricted or even blocked due tothe irregular shape of the vehicle, position of the pod or otherreasons. Multiple pods on a vehicle may provide the ability to collectdata from different perspectives, positions or obtain line of sight datanot previously recordable. In some implementations, the removable pod isnot connected to the vehicle control system of the vehicle to which thepod is mounted upon. In other words, the removable pod collects data anddoes not generate and/or send signals to control the vehicle.

FIG. 3 is illustrates an example of a computing system for trainingmachine learning model such as a neural network model, in whichimplementations disclosed herein may be implemented. Computing system172 can receive time stamped vehicle observations (i.e., a collection ofvehicle data and/or environmental data collected by one or moreautonomous vehicle(s) as well as one or more non-autonomous vehicle(s))via network 170. In many implementations computing system 172 includestemporal correlation engine 302, location engine 304, labeling engine306, neural network engine 310, training engine 314, and traininginstance engine 318. Temporal correlation engine 302, location engine304, labeling engine 306, neural network engine 310, training engine314, and training instance engine 318 are example components in whichtechniques described herein may be implemented and/or with whichsystems, components, and techniques described herein may interface. Theoperations performed by one or more engines 302, 304, 306, 310, 314, 318of FIG. 3 may be distributed across multiple computing systems. In someimplementations, one or more aspects of engines 302, 304, 306, 310, 314,318 may be combined into a single system and/or one or more aspects maybe implemented by computing system 172. For example, in some of thoseimplementations, aspects of temporal correlation engine 302 may becombined with aspects of labeling engine 306. Engines in accordance withmany implementations may each be implemented in one or more computingdevices that communication, for example, through a communicationnetwork. A communication network may include a wide area network such asthe Internet, one or more local area networks (“LAN”s) such as Wi-FiLANs, mesh networks, etc., and one or more bus subsystems. Acommunication network may optionally utilize one or more standardcommunication technologies, protocols, and/or inter-processcommunication techniques.

Computing system 172 can perform a variety of processing on vehicle data308. In many implementations, vehicle data 308 includes time stampedautonomous vehicle data (as described herein with respect to FIG. 1 ) aswell as time stamped additional vehicle data such as data collectedusing a removable hardware pod (as described herein with respect to FIG.2A). Temporal correlation engine 302 can (if necessary) synchronize timestamps between sets of data collected by separate vehicles collectingdata in the same environment. For example, vehicle data can be collectedfrom an autonomous vehicle and a removable hardware pod 250 mounted on anon-autonomous vehicle 280 co-present in the same environment at thesame time. While both vehicles were collecting data in an environmentsimultaneously, the time stamps appended to the data from one vehiclemay not correspond to the time stamps to the data collected from anothervehicle. In some implementations, time stamps in data collected by onevehicle can be shifted to correspond to time stamps in data collected byanother vehicle.

Location engine 304 can determine the proximity of vehicles within theenvironment (often at each time stamp) from vehicle data 308. In manyimplementations, the co-presence of vehicles can be determined using oneor more proximity sensors within a vehicle. Location engine 304 cancompare a signal generated by a proximity sensor in an autonomousvehicle with the signal generated by a proximity sensor in a removablehardware pod mounted on a non-autonomous vehicle to determine whetherthe autonomous vehicle and the non-autonomous vehicle are co-present inan environment. For example, signals from proximity sensors can indicatea non-autonomous vehicle is within twenty meters of an autonomousvehicle. In many implementations, signals from proximity sensors canindicate a wide variety of ranges including: not in range, within onemeter, within five meters, within ten meters, within fifty meters,within one hundred meters, within two hundred meters, etc. In some suchimplementations, only vehicle data where vehicles are within a thresholdlevel of proximity will be further processed (e.g., only data fromvehicles within a two hundred fifty meter range will be additionallyprocessed). Additionally or alternatively, vehicles can move in and outof a threshold range of proximity as they maneuver in the environment.For example, only data at time stamps where vehicles are in proximityrange can be additionally processed. In some such implementations,portions of vehicle data where vehicles are not in proximity can bediscarded.

Location engine 304 can additionally or alternatively determine vehiclelocations using vehicle data 308. In some implementations, 3Dpositioning sensor data, such as a position provided by a GPS system canlocalize vehicles within an environment. In other implementations,common landmarks can be used to localize the position of vehicles in anenvironment. Common landmarks can include a variety of objects includingstationary objects such as buildings, street signs, stop signs, trafficlights, mailboxes, trees, bushes, sections of a fence, etc. The distanceof an autonomous vehicle to the common landmark (e.g., using LIDAR data)can be determined from autonomous vehicle data. Similarly, the distanceof an additional vehicle to the common landmark can be determined fromthe additional vehicle. A distance between the autonomous vehicle andthe additional vehicle can be calculated at a specific time stamp usingthe distance of each vehicle to the common landmark. For example, acommon landmark such as a stop sign can be captured in autonomousvehicle data as well as in non-autonomous vehicle data. Data collectedby corresponding vehicle LIDAR units can determine a distance from eachvehicle to the stop sign at the same time stamp. The distance betweenthe autonomous vehicle and the non-autonomous vehicle can be calculatedusing the distance of each vehicle to the stop sign. Additionally oralternatively, the additional vehicle can determine its location in amap using a 3D reference frame (such as an earth-centered, earth-fixedreference frame). In some such implementations, an autonomous vehiclecan determine its location on the same map, with respect to the samereference frame, and/or one or more additional methods of determiningits location with respect to the same map as the additional vehicle.

Labeling engine 306 can generate labels (in some implementationsautomatically generate labels) for autonomous vehicle data using vehicledata collected from one or more additional vehicles, such as anon-autonomous vehicle equipped with a hardware pod 250. Computingsystem 172 can determine two vehicles are co-present in an environmentusing location engine 304. In some such implementations, labeling engine306 can determine instances of autonomous vehicle data which onlycaptures a single additional vehicle co-present in the environment(i.e., when the autonomous vehicle is known to be within a proximityrange of an additional vehicle, and only one vehicle is captured in theautonomous vehicle data, generally the additional vehicle will be thevehicle captured in the autonomous vehicle data). Data collected fromthe additional vehicle can be mapped to the location of the additionalvehicle in the instance of autonomous vehicle data at a common timestamp. For example, a brake light signal of non-autonomous vehicle canbe collected via a CAN bus and time stamped by a removable pod computingdevice (such as computing device 222 as illustrated in FIG. 2 ). A labelindicating the status of the brake lights of the non-autonomous vehiclecan be mapped to the position where the non-autonomous vehicle iscaptured in autonomous vehicle data to automatically generate a brakelight label for the non-autonomous vehicle at the corresponding timestamp. Additionally or alternatively, additional vehicle dataidentifying the non-autonomous vehicle, such as vehicle dimensions, canbe used to determine a precise bounding box around the non-autonomousvehicle in the autonomous vehicle observations. In otherimplementations, labeling engine 306 can utilize locations of twovehicles determined by location engine 304 (e.g., locations determinedusing GPS data collected form each vehicle and/or by localizing eachvehicle using a common landmark(s) in the environment). Similarly,additional data collected from the additional vehicle 280 can be mappedto its location in the autonomous vehicle data at corresponding timestamps.

Neural network engine 310 can train neural network model 312. Neuralnetwork model 312, in accordance with many implementations, can includea layer and/or layers of memory units where memory units each havecorresponding weights. A variety of neural network models can beutilized including feed forward neural networks, convolutional neuralnetworks, recurrent neural networks, radial basis functions, otherneural network models, as well as combinations of several neuralnetworks. Additionally or alternatively, neural network model 312 canrepresent a variety of machine learning networks in addition to neuralnetworks such as support vector machines, decision trees, Bayesiannetworks, other machine learning techniques, and/or combinations ofmachine learning techniques. Training neural network model 312 inaccordance with many implementations described herein can utilize neuralnetwork engine 310, training engine 314, and training instance engine318. Neural network models can be trained for a variety of autonomousvehicle tasks including determining a target autonomous vehiclelocation, generating one or more signals to control an autonomousvehicle, identifying objects within the environment of an autonomousvehicle, etc. For example, a neural network model can be trained toidentify traffic lights in the environment with an autonomous vehicle.As a further example, a neural network model can be trained to predictthe make and model of other vehicles in the environment with anautonomous vehicle. In many implementations, neural network models canbe trained to perform a single task. In other implementations, neuralnetwork models can be trained to perform multiple tasks.

Training instance engine 318 can generate training instances to trainthe neural network model. A training instance can include, for example,an instance of autonomous vehicle data where the autonomous vehicle candetect an additional vehicle using one or more sensors and a labelcorresponding to data collected from the additional vehicle. Trainingengine 314 applies a training instance as input to neural network model312. In a variety of implementations, neural network model 312 can betrained using supervised learning, unsupervised learning, andsemi-supervised learning. Additionally or alternatively, neural networkmodels in accordance with some implementations can be deep learningnetworks including recurrent neural networks, convolutional neuralnetworks, networks that are a combination of multiple networks, etc. Forexample, training engine 314 can generate predicted neural network modeloutput by applying training input to the neural network model 312.Additionally or alternatively, training engine 314 can compare predictedneural network model output with neural network model known output fromthe training instance and, using the comparison, update one or moreweights in neural network model 312 (e.g., backpropagate the differenceover the entire neural network model 312).

In many implementations, overfitting can occur when neural network model312 is trained such that it too closely learns a particular set oftraining instances and therefore fails to respond well to unknowntraining instances. Overfitting can be prevented in autonomous vehicledata where sensors detect vehicles carrying removable hardware pod(s) ina variety of ways. For example, using a removable hardware pod of avariety of types of vehicles can generate a wider variety of data and,for example, can prevent a neural network model from learning how anautonomous vehicle interacts with only one type of non-autonomousvehicle. Additionally or alternatively, the removable hardware pod canbe identified in autonomous vehicle data so the neural network modeldoes not simply become good at recognizing hardware pods. For example,using a known dimensions of the non-autonomous vehicle and the positionof the hardware pod on the non-autonomous vehicle, a precise boundingbox can be drawn around the non-autonomous vehicle in the autonomousvehicle data which can exclude the hardware pod. Additionally oralternatively, one or more image processing techniques can be used tomask removable hardware pods out of autonomous vehicle data.Furthermore, since the location of the hardware pod is known, the areacontaining the hardware pod can be subtracted out of autonomous vehicledata and not used in training instances.

To further prevent overfitting in neural network models in accordancewith many implementations, the location where a hardware pod is mountedon an non-autonomous vehicle can be varied, the shape of the hardwarepods itself can be varied, the sensor suite of the hardware pod can bespread out over the vehicle (e.g., the hardware pod can be givententacles to spread sensors out over the top of a vehicle, etc.).Additionally or alternatively, the LIDAR unit (i.e., the sensor whichtypically has the tallest profile in the hardware suite) can be movedinside the vehicle. Furthermore, in some implementations the LIDAR unitcan be replaced with a camera. It can be more computationally intensiveto perform common landmark based localization using only a camera, butas a tradeoff using only a camera can help prevent overfitting in theneural network model.

FIG. 4 illustrates another example of a computing system for testing atrained neural network model in which implementations disclosed hereinmay be implemented. Computing system 172, temporal correlation engine302, location engine 304, labeling engine 306, and vehicle data 308 aredescribed with respect to FIG. 3 . Neural network engine 310, testingengine 406, and testing instance engine 410 in accordance withimplementations can be utilized to generate testing instances forautonomous vehicle data including a label corresponding to an additionalvehicle present in the autonomous vehicle data, as well as to test atrained neural network model 404. In some implementations, trainedneural network model 404 can generate predicted output for a singleautonomous vehicle task. In other implementations, trained neuralnetwork model 404 can generate predicted output for multiple autonomousvehicle tasks. Testing instance engine 410 can generate testinginstances 408 using labeled autonomous vehicle data collected from anautonomous vehicle and an additional vehicle performing the specificautonomous vehicle task neural network model 404 is trained for.

A testing instance, for example, can include an instance of autonomousvehicle data where an additional vehicle is detected by one or moresensors of the autonomous vehicle, and a label corresponding to datacollected by the additional vehicle. Testing engine 406 can apply atesting instance as input to neural network model 404. Predicted outputgenerated by applying a testing instance to neural network model 404 canbe compared with the known output for the testing instance (i.e., thelabel) to update an accuracy value (e.g., an accuracy percentage) forthe neural network model. Generating testing instances and using testinginstances to determine an accuracy of a trained neural network model inaccordance with various implementations is described herein in process1000 in FIG. 10 .

FIG. 5 illustrates an example autonomous vehicle and an examplenon-autonomous vehicle mounted with a removable hardware pod inaccordance with implementations described herein. Image 500 includes ascene of autonomous vehicle 502, non-autonomous vehicle 506 mounted withremovable hardware pod 508, as well as a variety of common landmarks512, 514, and 516. Autonomous vehicle 502 is traveling in direction 504on a road. Non-autonomous vehicle 506, which is traveling in direction510 on the same road as autonomous vehicle 502. Vehicles 502 and 506 cantravel in a variety of directions. For example, autonomous vehicle 504and non-autonomous vehicle 506 are traveling in opposite directionstowards one another in adjacent lanes in the road, where direction 504is substantially parallel and opposite to direction 510. Furthermore,common landmarks in an environment with an autonomous vehicle and anon-autonomous vehicle can be used to localize the position of thevehicles. Common landmarks can include any of a variety of stationaryobjects detectable by both vehicles such as tree(s) 512, building(s)514, fire hydrant(s) 516, etc. In many implementations, data collectedby removable pod 508 can be mapped to data collected by autonomousvehicle 502 to label autonomous vehicle data. For example, the color ofthe non-autonomous vehicle can be mapped to the location of thenon-autonomous vehicle in the autonomous vehicle data to generate acolor label. As another example, the steering angle of thenon-autonomous vehicle (collected via the CAN bus) at a specific timestamp can be mapped to the location of the non-autonomous vehicle in theautonomous vehicle data to generate a steering angle label.

FIG. 6 illustrates another example autonomous vehicle and non-autonomousvehicle mounted with a removable hardware pod in accordance withimplementations described herein. Image 600 includes a scene ofautonomous vehicle 602 traveling in direction 604, non-autonomousvehicle 606 mounted with removable hardware pod 608 traveling indirection 610, as well as common landmarks 612, 614, and 616. Forexample, autonomous vehicle 602 and non-autonomous vehicle 610 aretraveling in substantially the same direction in adjacent lanes on aroad. Common landmarks can include: tree(s) 612, building(s) 614, firehydrant 616, etc. In the illustrated example, non-autonomous vehicle 606is between autonomous vehicle 602 and fire hydrant 616. In some suchimplementations, the view of fire hydrant 616 can be partially and/orfully occluded from autonomous vehicle 602 (i.e., one or more sensors inautonomous vehicle 602 cannot detect a portion and/or any of firehydrant 616). While autonomous vehicle 602 cannot detect fire hydrant616 at particular time stamps, data collected by removable pod 608 canbe used, along with known locations of autonomous vehicle 602 andnon-autonomous vehicle 606, to determine the distance from theautonomous vehicle to the fire hydrant. In other words, data collectedby removable hardware pod 608 can extend the field of vision ofautonomous vehicle 602 to objects outside of autonomous vehicle 602'sdirect line of sight. In some such implementations, the distance from anautonomous vehicle to an occluded object in the environment can bedetermined by calculating the distance of the autonomous vehicle to thenon-autonomous vehicle using the autonomous vehicle data, the distancefrom the non-autonomous vehicle to the object using the additionalvehicle data, and using the distance from the autonomous vehicle to thenon-autonomous vehicle and the distance from the non-autonomous vehicleto the object to calculate the distance between the object and theautonomous vehicle.

FIG. 7 illustrates another example autonomous vehicle and non-autonomousvehicle mounted with a removable hardware pod in accordance withimplementations described herein. Image 700 includes a scene ofautonomous vehicle 702 traveling in direction 704 and non-autonomousvehicle 706 mounted with removable hardware pod 708 traveling indirection 710, as well as common landmarks: tree(s) 716, building(s)718, fire hydrant(s) 720, etc. Non-autonomous vehicle 706 is travelingin front of autonomous vehicle 702 in the same lane of a road.

Image 700 further illustrates a previous position 712 of thenon-autonomous vehicle, where the non-autonomous vehicle has moved in adirection 714 from original position 712 (e.g., moving in direction 714can cause the non-autonomous vehicle to change lanes and move into aposition in front of the autonomous vehicle). In a variety ofimplementations, autonomous vehicles and/or non-autonomous vehicles canperform one or more autonomous vehicle tasks to generate labeledautonomous vehicle data which can be used as training instances (asdescribed in FIG. 3 ) and/or testing instances (as described in FIG. 4 )for a neural network model. Autonomous vehicle tasks can include avariety of task to control an autonomous vehicle, as well as enabling anautonomous vehicle to interact with its environment (e.g., collectingdata about an environment, identifying objects within the environment,etc.). For example, an autonomous vehicle can learn how to interact withother vehicles driving in the same environment (such as non-autonomousvehicle 706 moving in the same lane in front of autonomous vehicle 702).

Labeled autonomous vehicle data in accordance with implementationsdescribed herein can, for example, train a neural network model togenerate one or more control signals for how the autonomous vehicleshould respond when another vehicle moves in front of it. As a furtherexample, a car quickly moving in front of the autonomous vehicle with aminimal distance between it and the autonomous vehicle (i.e., the othervehicle cutting the autonomous vehicle off) might not happen asfrequently (and thus can be considered an edge case). It can take manymonths to generate sufficient labeled autonomous vehicle data whilewaiting for an autonomous vehicle to be cut off by other vehiclesorganically. In many implementations, a non-autonomous vehicle equippedwith a removable hardware pod can repeatedly cut off an autonomousvehicle. Sufficient labeled autonomous vehicle data for generatingtesting instances and/or training instances for a neural network modelcan be generated by the non-autonomous vehicle and autonomous vehicle ina matter of hours and/or days.

FIG. 8 illustrates an example autonomous vehicle and multiplenon-autonomous vehicles mounted with a removable hardware pod inaccordance with implementations described herein. Image 800 is a sceneincluding autonomous vehicle 802 at one corner of a four way stop. Threenon-autonomous vehicles 806, each mounted with a removable hardware pod806 are stopped at the other corners of the four way stop. Additionally,image 800 includes four stop signs 810, one located at each corner ofthe four way stop. For example, autonomous vehicle data can be collectedfrom the autonomous vehicle while additional vehicle data is collectedfor each of the non-autonomous vehicles using a removable pod. The brakelights of other cars at a four way stop are typically not in the fieldof view of an autonomous vehicle. Removable hardware pods can capturebrake light data from the non-autonomous vehicle's via thenon-autonomous vehicle's CAN busses, and a brake light signal indicating(e.g., indicating if a brake light is on or off) can be mapped to thenon-autonomous vehicle in the in the autonomous vehicle data. In otherwords, removable hardware pods can expand the field of view ofautonomous vehicle sensors, as well as quickly capture large amounts ofdata simultaneously in a scene.

As described above with respect to FIG. 7 , autonomous vehicles canperform a variety of autonomous vehicle tasks including an autonomousvehicle interacting with other vehicles at a four way stop. In thepresent example, the three non-autonomous vehicles 806 can be located inthe data collected by autonomous vehicle 802. In accordance withimplementations described herein, data collected from a removablehardware pod 804 can be mapped to the location of the correspondingnon-autonomous vehicle in the autonomous vehicle data at specific timestamps. Removable hardware pods 804 can collect a variety of dataincluding data generally inaccessible to an autonomous vehicle (e.g.,data collected from a non-autonomous vehicle CAN bus). This additionaldata can be used to create more robust training instances and/or testinginstances for autonomous vehicle neural network models in accordancewith many implementations. Additionally or alternatively, multipleadditional vehicles collecting data in the same environment as anautonomous vehicle can quickly collect a wide diversity of data. In someimplementations, additional vehicle data can be mapped to the autonomousvehicle data to extend the range of vision of the autonomous vehicle.For example, additional vehicle data collected from a non-autonomousvehicle in front of the autonomous vehicle can include informationoutside of the range of view of the autonomous vehicle's sensors. Thisdata collected by the non-autonomous vehicle beyond the field of view ofthe autonomous vehicle can be mapped to the data collected by theautonomous vehicle, thus increasing the range of data collected for usein training the autonomous vehicle.

Referring to FIG. 9 , an example process 900 for practicing selectedaspects of the present disclosure in accordance with variousimplementations is disclosed. For convenience, the operations of theflowchart are described with reference to a system that performs theoperations. This system may include various components of variousdevices, including those described in FIGS. 1-4 . Moreover, whileoperations of process 900 are shown in a particular order, this is notmeant to be limiting. One or more operations, elements, and/or steps maybe reordered, omitted, and/or added.

At block 902, the system receives autonomous vehicle data. Autonomousvehicle data can be collected from one or more autonomous vehiclesensors 130 (as illustrated in FIG. 1 ) and can include vision data suchas LIDAR data and/or an image from a camera. In some suchimplementations, the additional vehicle as viewed by the autonomousvehicle is present in the vision data.

At block 904, the system receives additional vehicle data. In someimplementations additional vehicle data can be collected from a secondautonomous vehicle sensor suite in a second autonomous vehicle. In otherimplementations, vehicle data can be collected from a removable hardwarepod mounted on a non-autonomous vehicle (as illustrated in FIGS. 2A-2C).

At block 906, the system temporally correlates the autonomous vehicledata with the additional vehicle data for vehicles in the sameenvironment. For example, data sets collected at the same time in thesame environment can have different time stamps. The time stamp in one(or more) data sets can be shifted to correspond with a particular dataset (e.g., shift one or more data sets corresponding to additionalvehicles such that the additional vehicle data set(s) match up with thedata set for an autonomous vehicle). Temporal correlation engine 302 (asillustrated in FIG. 3 and FIG. 4 ) can temporally correlate autonomousvehicle data with additional vehicle data.

At block 908, the system localizes the autonomous vehicle and theadditional vehicle in an environment. In many implementations, a systemcan determine if two vehicles are co-present in an environment in postprocessing using signals collected from one or more proximity sensors ineach vehicle. Additionally or alternatively, GPS data in the autonomousvehicle data can localize the autonomous vehicle at each time stamp.Similarly, GPS data in the additional vehicle data can localize theautonomous vehicle at each time stamp. Furthermore, a common landmarkcan be found at the same time stamp in autonomous vehicle data andadditional vehicle data. A distance from the common landmark to eachvehicle can be determined using each vehicles corresponding LIDAR data.The distance between the autonomous vehicle and the additional vehiclecan be calculated using the distance of each vehicle to the commonlandmark. Location engine 304 (as illustrated in FIG. 3 and FIG. 4 ) canlocalize an autonomous vehicle and at least one additional vehiclewithin an environment.

At block 910, the system determines whether the autonomous vehicle andthe at least one additional vehicle are in range of each other usingdeterminations made by the system in block 908. For example, one or moreproximity sensors can indicate two vehicles are within range of eachother. Additionally or alternatively, proximity sensor(s) can indicatetwo vehicles are not within range of each other. In manyimplementations, the location of the autonomous vehicle and theadditional vehicle can be used to determine if the two vehicles are inrange of each other. For example, in many implementations the distancebetween the two vehicles is two hundred fifty meters or less to beconsidered in range of each other. In various implementations, a systemcan determine two vehicles are in range by finding an instance of theadditional vehicle with the removable hardware pod in the vision data ofthe autonomous vehicle.

At block 912, the system labels an instance of autonomous vehicle dataat a time stamp by mapping additional vehicle data at the time stamp tothe location of the additional vehicle in the autonomous vehicle data.In some implementations, labeled autonomous vehicle data can beautomatically generated. Labeling engine 306 (as illustrated in FIG. 3and FIG. 4 ) can label autonomous vehicle data in accordance with manyimplementations. Labels can include a variety of information about theadditional vehicle including both static and dynamic information. Staticinformation can be collected from a user, for example while a user iscalibrating the position of a removable hardware pod while mounting theremovable hardware pod on a vehicle, and can include known informationabout the additional vehicle such as the vehicle make, model, color,dimensions, position of the mounted removable hardware pod, etc.Similarly, dynamic information can change at different times and caninclude a variety of information including LIDAR point cloud data at aspecific time, an image captured by a camera, 3D positioninginformation, data from the IMU, as well as real time vehicle informationcollected from the additional vehicle CAN bus such as vehicle velocity,one or more steering control signals, brake light status, etc. In manyimplementations, a label in autonomous vehicle data can be utilized intraining a neural network model as a known output of the instance of theautonomous vehicle data. In some implementations, an individual instanceof autonomous vehicle data can have many labels, each indicating aseparate property of the additional vehicle captured in the instance ofautonomous vehicle data. In other implementations, a label can contain amultitude of information captured by the additional vehicle.Additionally or alternatively, data collected from multiple additionalvehicles can be used to generate labels in a single instance ofautonomous vehicle data.

At block 914, the system determines whether any additional instances ofautonomous vehicle data still need to be labeled. If so, the systemproceeds back to block 912, and labels an additional instance of vehicledata. Autonomous vehicle data labeled in accordance with process 900 canbe used to generate training data and/or testing data for a neuralnetwork model.

Referring to FIG. 10 , an example process 1000 for practicing selectedaspects of the present disclosure in accordance with variousimplementations is disclosed. For convenience, the operations of theflowchart are described with reference to a system that performs theoperations. This system may include various components of variousdevices, including those described in FIGS. 1-4 . Moreover, whileoperations of process 1000 are shown in a particular order, this is notmeant to be limiting. One or more operations, elements, and/or steps maybe reordered, omitted, and/or added.

At block 1002, the system generates neural network model testinginstances from labeled autonomous vehicle data where each testinginstance includes an instance of autonomous vehicle data and acorresponding label. Labeled autonomous vehicle data can be generated inaccordance with process 900 described herein.

At block 1004, the system selects a testing instance generated at block1002.

At block 1006, the system applies the autonomous vehicle portion of thetesting instance as input to a neural network model trained to performthe specific autonomous vehicle task to generate predicted output.

At block 1008, the system updates the accuracy of the trained neuralnetwork model based on the predicted output generated by the autonomousvehicle data portion of testing instance and the label of the testinginstance (i.e., known output of the testing instance). In someimplementations, model accuracy can be expressed as a percentage where anetwork with 100% accuracy is always accurate and a network with 0%accuracy is never accurate. In some such implementations, the accuracyvalue can be incrementally updated after each testing instance. Thetrained network model can optionally be updated by backpropagation anerror over the trained neural network model to update one more weightsof the neural network model. Additionally or alternatively, one instanceof the trained neural network model can be updated using traininginstances, and a second instance of the trained neural network model canbe used to test the accuracy of the trained neural network (i.e.,generating both an accuracy of the original trained neural network aswell as an updated trained neural network).

At block 1010, the system determines if any additional testing instancesremain. If so, the system proceeds back to block 1004, selects anadditional testing instance, then performs blocks 1006 and 1008 based onthe additional unprocessed testing instance. In some implementations, atblock 1010 the system may determine not to process any additionalunprocessed testing instances if one or more testing criteria has beensatisfied (e.g., a threshold duration of testing has occurred and/or alltesting instances have been processed).

While several implementations have been described and illustratedherein, a variety of other means and/or structures for performing thefunction and/or obtaining the results and/or one or more of theadvantages described herein may be utilized, and each of such variationsand/or modifications is deemed to be within the scope of theimplementations described herein. More generally, all parameters,dimensions, materials, and configurations described herein are meant tobe exemplary and that the actual parameters, dimensions, materials,and/or configurations will depend upon the specific application orapplications for which the teachings is/are used. Those skilled in theart will recognize, or be able to ascertain using no more than routineexperimentation, many equivalents to the specific implementationsdescribed herein. It is, therefore, to be understood that the foregoingimplementations are presented by way of example only and that, withinthe scope of the appended claims and equivalents thereto,implementations may be practiced otherwise than as specificallydescribed and claimed. Implementations of the present disclosure aredirected to each individual feature, system, article, material, kit,and/or method described herein. In addition, any combination of two ormore such features, systems, articles, materials, kits, and/or methods,if such features, systems, articles, materials, kits, and/or methods arenot mutually inconsistent, is included within the scope of the presentdisclosure.

What is claimed is:
 1. A method implemented by one or more processorsfor generating testing instances for a machine learning model trainedfor autonomous vehicle control, the method comprising: receivingautonomous vehicle data using an autonomous vehicle sensor suite on anautonomous vehicle; receiving truck data using a plurality of trucksensor suites on a truck, wherein the truck is in an environment withthe autonomous vehicle, wherein at least one of the plurality of trucksensor suites on the truck is a removable hardware pod that is mountedonto the truck; determining an instance of autonomous vehicle datawherein at least one of sensors of the autonomous vehicle sensor suitedetect the truck in the environment; determining one or more instancesof truck data which temporally correspond with the instance ofautonomous vehicle data where the autonomous vehicle detects the truck;and generating a plurality of testing instances for a trained machinelearning model, wherein each testing instance includes (1) the instanceof autonomous vehicle data where the autonomous vehicle detects thetruck and (2) one or more labels, where each label indicates asubstantially corresponding current state of at least one attribute ofthe truck captured via a corresponding removable hardware pod, whereinthe substantially corresponding current state is determined using atleast one of the determined corresponding one or more instances of truckdata.
 2. The method of claim 1, wherein the autonomous vehicle is anautonomous truck.
 3. The method of claim 1, wherein the truck is anautonomous truck.
 4. The method of claim 1, wherein the truck is anon-autonomous truck.
 5. The method of claim 1, wherein at least one ofthe removable hardware pods is mounted in a vehicle cabin of the truck.6. The method of claim 5, wherein one or more of the removable hardwarepods are mounted onto a body of the truck.
 7. The method of claim 1,wherein determining the instance of autonomous vehicle data wherein atleast one of the sensors of the autonomous vehicle sensor suite detectthe truck in the environment comprises: detecting, using a proximitysensor of the autonomous vehicle sensor suite, at least one detectableremovable hardware pod of the plurality of removable hardware pods. 8.The method of claim 7, wherein detecting, using the proximity sensor ofthe autonomous vehicle sensor suite, at least one detectable removablehardware pod of the plurality of removable hardware pods furthercomprises: failing to detect, using the proximity sensor of theautonomous vehicle sensor suite, at least one undetectable removablehardware pod of the plurality of removable hardware pods, where the atleast one undetectable removable hardware pod is distinct from the atleast one detectable removable hardware pod.
 9. The method of claim 8,further comprising: determining one or more additional instances oftruck data captured using the at least one undetectable removablehardware pod temporally correlating with the instance of autonomousvehicle data based on detecting, using the proximity sensor of theautonomous vehicle sensor suite, the at least one detectable removablehardware pod of the plurality of removable hardware pods, wherein the atleast one detectable removable hardware pod is in the same plurality ofremovable hardware pods as the at least one undetectable removablehardware pod.
 10. The method of claim 1, wherein determining theinstance of autonomous vehicle data wherein at least one of the sensorsof the autonomous vehicle sensor suite detect the truck in theenvironment further comprises: detecting, in the instance of autonomousvehicle data, at least one detectable removable hardware pod of theplurality of removable hardware pods; and failing to detect, in theinstance of autonomous vehicle data, at least one undetectable removablehardware pod of the plurality of removable hardware pods, wherein the atleast one undetectable removable hardware pod is distinct from the atleast one detectable removable hardware pod.
 11. The method of claim 10,further comprising: identifying a landmark in the one or more instancesof truck data captured via the at least one detectable removablehardware pod of the plurality of removable hardware pods; identifyingthe landmark in one or more additional instances of truck data capturedvia the at least one undetectable removable hardware pod of theplurality of removable hardware pods; and determining the one or moreadditional instances of truck data temporally corresponding with theinstance of autonomous vehicle data where the autonomous vehicle detectsthe truck based on identifying the landmark in the one or moreadditional instances of truck data captured via the at least oneundetectable removable hardware pod.
 12. The method of claim 1, whereingenerating the plurality of testing instances for the trained machinelearning model comprises: for each testing instance, applying theinstance of autonomous vehicle data where the autonomous vehicle detectsthe truck as an input of the trained machine learning model to produce apredicted output of the trained machine learning model.
 13. The methodof claim 12, wherein generating the plurality of testing instances forthe trained machine learning model further comprises: for each testinginstance, applying one or more of the labels as a known output of thetrained machine learning model.
 14. The method of claim 13, furthercomprising: determining an accuracy value of the trained machinelearning model by comparing the predicted output of the trained machinelearning model with the known output of the trained machine learningmodel.
 15. The method of claim 14, further comprising: updating theaccuracy value of the trained machine learning model based on adifference between the predicted output of the trained machine learningmodel and the known output of the trained machine learning model. 16.The method of claim 15, wherein: the accuracy value is incrementallyupdated after each testing instance.
 17. The method of claim 1, wherein:the trained machine learning model is a neural network model.
 18. Asystem comprising one or more processors and a non-transitory memoryoperably coupled with the one or more processors, wherein the memorystores instructions that, in response to execution of the instructionsby the one or more processors, cause the one or more processors toperform the following operations: receiving autonomous vehicle datausing an autonomous vehicle sensor suite on an autonomous vehicle;receiving truck data using a plurality of truck sensor suites on atruck, wherein the truck is in an environment with the autonomousvehicle, wherein at least one of the plurality of truck sensor suites onthe truck is a removable hardware pod that is mounted onto the truck;determining an instance of autonomous vehicle data wherein at least oneof the sensors of the autonomous vehicle sensor suite detect the truckin the environment; determining one or more instances of truck datawhich temporally correspond with the instance of autonomous vehicle datawhere the autonomous vehicle detects the truck; and generating aplurality of testing instances for a trained machine learning model,wherein each testing instance includes (1) the instance of autonomousvehicle data where the autonomous vehicle detects the truck and (2) oneor more labels, where each label indicates a substantially correspondingcurrent state of at least one attribute of the truck captured via acorresponding removable hardware pod, wherein the substantiallycorresponding current state is determined using at least one of thedetermined corresponding one or more instances of truck data.
 19. Atleast one non-transitory computer-readable medium comprisinginstructions that, in response to execution of the instructions by oneor more processors, cause the one or more processors to perform thefollowing operations: receiving autonomous vehicle data using anautonomous vehicle sensor suite on an autonomous vehicle; receivingtruck data using a plurality of truck sensor suites on a truck, whereinthe truck is in an environment with the autonomous vehicle, wherein atleast one of the plurality of truck sensor suites on the truck is aremovable hardware pod that is mounted onto the truck; determining aninstance of autonomous vehicle data wherein at least one of sensors ofthe autonomous vehicle sensor suite detect the truck in the environment;determining one or more instances of truck data which temporallycorrespond with the instance of autonomous vehicle data where theautonomous vehicle detects the truck; and generating a plurality oftesting instances for a trained machine learning model, wherein eachtesting instance includes (1) the instance of autonomous vehicle datawhere the autonomous vehicle detects the truck and (2) one or morelabels, where each label indicates a substantially corresponding currentstate of at least one attribute of the truck captured via acorresponding removable hardware pod, wherein the substantiallycorresponding current state is determined using at least one of thedetermined corresponding one or more instances of truck data.